Symcap
Teegan Innis
September 14, 2016
Project Summary
This project aimed to look at the spatial variability of Symbiodinium clades C and D in the Kane’ohe Bay, O’ahu, Hawai’i population of Montipora capitata. We investigated the distribution of symbionts at scales ranging from location in the bay to location on an individual reef. We also looked at differences among reef types (fringing vs. patch), colony color morph (brown vs. orange) and depth. Heterogeneous mixtures of symbiont clades were considered in the analysis for spatial patterns. By investigating spatial variability of Symbiodinium, we furthered the understanding of stress-response potential in Kane’ohe Bay.
Library Packages
setwd("~/Symcap")
library(data.table)
library(devtools)
library(plyr)
library(reshape2)
library(popbio)
library(RgoogleMaps)
library(plotrix)
library(zoo)
library(rgdal)
library(car)
library(scales)Import/Merge Field and qPCR Data
Coral_Data <- read.csv("Coral_Collection.csv")
Coral_Data$Depth..m. <- as.numeric(as.character(Coral_Data$Depth..m.))
source_url("https://raw.githubusercontent.com/jrcunning/steponeR/master/steponeR.R")
Mcap.plates <- list.files(path = "qPCR_data", pattern = "txt$", full.names = T)
Mcap <- steponeR(files = Mcap.plates, delim="\t",
target.ratios=c("C.D"),
fluor.norm = list(C=2.26827, D=0),
copy.number=list(C=33, D=3))
Mcap <- Mcap$result
Mcap <- Mcap[grep("+", Mcap$Sample.Name, fixed=T, invert = T), ]
Mcap <- Mcap[grep("NTC", Mcap$Sample.Name, fixed = T, invert = T), ]
Mcap <- Mcap[grep("PCT", Mcap$Sample.Name, fixed = T, invert = T), ]
colnames(Mcap)[which(colnames(Mcap)=="Sample.Name")] <- "Colony"
Mcap$fail <- ifelse(Mcap$C.reps < 2 & Mcap$D.reps < 2, TRUE, FALSE)
fails <- Mcap[Mcap$fail==TRUE, ]
Mcap <- Mcap[which(Mcap$fail==FALSE),]
Mcap$C.D[which(Mcap$C.reps<2)] <- -Inf
Mcap$C.D[which(Mcap$D.reps<2)] <- Inf
Mcap <- Mcap[with(Mcap, order(Colony)), ]
Mcap$propC <- Mcap$C.D / (Mcap$C.D+1)
Mcap$propD <- 1-Mcap$propC
Mcap$propD[which(Mcap$C.D==-Inf)] <-1
Mcap$propC[which(Mcap$C.D==-Inf)] <-0
Mcap$propD[which(Mcap$C.D==Inf)] <-0
Mcap$propC[which(Mcap$C.D==Inf)] <-1
Mcap$Dom <- ifelse(Mcap$propC>Mcap$propD, "C", "D")
Symcap<-merge(Coral_Data, Mcap, by="Colony", all=T)
Symcap <- Symcap[with(Symcap, order(Colony)), ]
Symcap$Mix <- factor(ifelse(Symcap$propC>Symcap$propD, ifelse(Symcap$propD!=0, "CD", "C"), ifelse(Symcap$propD>Symcap$propC, ifelse(Symcap$propC!=0, "DC", "D"), NA)), levels = c("C", "CD", "DC", "D"))
Symcap$Reef.Area <- ifelse(Symcap$Reef.Area!="Top", yes = "Slope", no = "Top")Adjust Depth by Mean Sea Level
To account for the influence of tides, depth was adjusted according to the difference in sea level from the mean sea level on each collection day at 6-minute intervals. Mean sea level was obtained from NOAA tide tables for Moku o Lo’e.
JuneTide=read.csv("Station_1612480_tide_ht_20160601-20160630.csv")
JulyTide=read.csv("Station_1612480_tide_ht_20160701-20160731.csv")
Tide<-rbind(JuneTide, JulyTide)
Tide$Time <- as.POSIXct(Tide$TimeUTC, format="%Y-%m-%d %H:%M:%S", tz="UTC")
attributes(Tide$Time)$tzone <- "Pacific/Honolulu"
Symcap$Time2 <- as.POSIXct(paste(as.character(Symcap$Date), as.character(Symcap$Time)), format="%m/%d/%y %H:%M", tz="Pacific/Honolulu")
Symcap$Time=Symcap$Time2
Round6 <- function (times) {
x <- as.POSIXlt(times)
mins <- x$min
mult <- mins %/% 6
remain <- mins %% 6
if(remain > 3L) {
mult <- mult + 1
} else {
x$min <- 6 * mult
}
x <- as.POSIXct(x)
x
}
Symcap$Time.r <- Round6(Symcap$Time)
Tide$Time.r <- Tide$Time
merged<-merge(Symcap, Tide, by="Time.r", all.x=T)
merged$newDepth <- merged$Depth..m.- merged$TideHTDominant Symbiont by Reef Area
Symcap$Reef.Area <- ifelse(Symcap$Reef.Area!="Top", yes = "Slope", no = "Top")
results=table(Symcap$Dom, Symcap$Reef.Area)
chisq.test(results)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: results
## X-squared = 136.26, df = 1, p-value < 2.2e-16prop.table(results, margin = 2)##
## Slope Top
## C 0.7767857 0.3294574
## D 0.2232143 0.6705426par(mar=c(4, 4, 2, 6))
barplot(prop.table(results, margin = 2), col = c("gray10", "gray100"), xlab = "Reef Area", ylab = "Symbmiont Proportion")
legend("topright", legend=c("C", "D"), fill=c("gray10", "gray100"), inset = c(-.2, 0), xpd = NA)
Dominant Symbiont by Color Morph
results=table(Symcap$Dom, Symcap$Color.Morph)
chisq.test(results)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: results
## X-squared = 164.96, df = 1, p-value < 2.2e-16prop.table(results, margin = 2)##
## Brown Orange
## C 0.8896321 0.4103194
## D 0.1103679 0.5896806par(mar=c(4, 4, 2, 6))
barplot(prop.table(results, margin = 2), col = c("gray10", "gray100"), xlab = "Color Morph", ylab = "Symbiont Proportion")
legend("topright", legend=c("C", "D"), fill=c("gray10", "gray100"), inset = c(-.2, 0), xpd = NA)
Symbiont Community Composition by Color Morph
results=table(Symcap$Mix, Symcap$Color.Morph)
chisq.test(results)##
## Pearson's Chi-squared test
##
## data: results
## X-squared = 167.44, df = 3, p-value < 2.2e-16prop.table(results, margin = 2)##
## Brown Orange
## C 0.762541806 0.361179361
## CD 0.127090301 0.049140049
## DC 0.107023411 0.570024570
## D 0.003344482 0.019656020par(mar=c(4, 4, 2, 6))
barplot(prop.table(results, margin = 2), col = c("gray10", "gray50", "gray85", "gray100"), xlab = "Color Morph", ylab = "Symbiont Community Composition")
legend("topright", legend=c("C", "CD", "DC", "D"), fill=c("gray10", "gray50", "gray85", "gray100"), inset = c(-.2, 0), xpd = NA)
Symbiont Community Composition by Reef Area
Symcap$Reef.Area <- ifelse(Symcap$Reef.Area!="Top", yes = "Slope", no = "Top")
results=table(Symcap$Mix, Symcap$Reef.Area)
chisq.test(results)##
## Pearson's Chi-squared test
##
## data: results
## X-squared = 138.97, df = 3, p-value < 2.2e-16prop.table(results, margin = 2)##
## Slope Top
## C 0.678571429 0.275193798
## CD 0.098214286 0.054263566
## DC 0.214285714 0.651162791
## D 0.008928571 0.019379845par(mar=c(4, 4, 2, 6))
barplot(prop.table(results, margin = 2), col = c("gray10", "gray50", "gray85", "gray100"), xlab = "Reef Area", ylab = "Symbiont Community Composition")
legend("topright", legend=c("C", "CD", "DC", "D"), fill=c("gray10", "gray50", "gray85", "gray100"), inset = c(-.2, 0), xpd = NA)
Symbmiont Community Composition by Dominant Symbiont
results=table(Symcap$Mix, Symcap$Dom)
chisq.test(results)##
## Pearson's Chi-squared test
##
## data: results
## X-squared = 707, df = 3, p-value < 2.2e-16prop.table(results, margin = 2)##
## C D
## C 0.86635945 0.00000000
## CD 0.13364055 0.00000000
## DC 0.00000000 0.96703297
## D 0.00000000 0.03296703par(mar=c(4, 4, 2, 6))
barplot(prop.table(results, margin = 2), col = c("gray 10", "gray 85", "gray 40", "gray100"), xlab = "Dominant Symbiont", ylab = "Symbiont Mixture Proportion")
legend("topright", legend=c("C", "CD", "DC", "D"), fill=c("gray10", "gray85", "gray40", "gray100"), inset = c(-.2, 0), xpd = NA)
Proportion of D When Present in Mixture
propD <- merged$propD[which(merged$propD > 0 & merged$propD < 1)]
hist(propD, xlab = "Proportion of Clade D", ylab = "Number of Samples", main = "", col = "gray75")
Color Morph by Reef Area
Symcap$Reef.Area <- ifelse(Symcap$Reef.Area!="Top", yes = "Slope", no = "Top")
results=table(Symcap$Color.Morph, Symcap$Reef.Area)
chisq.test(results)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: results
## X-squared = 81.109, df = 1, p-value < 2.2e-16prop.table(results, margin = 2)##
## Slope Top
## Brown 0.5523385 0.2015504
## Orange 0.4476615 0.7984496par(mar=c(4, 4, 2, 6))
barplot(prop.table(results, margin = 2), col = c("gray10", "gray100"), xlab = "Reef Area", ylab = "Color Morph Proportion")
legend("topright", legend=c("Brown", "Orange"), fill=c("gray10", "gray100"), inset = c(-.2, 0), xpd = NA)
Color Morph by Reef ID
results=table(Symcap$Color.Morph, Symcap$Reef.ID)
chisq.test(results)##
## Pearson's Chi-squared test
##
## data: results
## X-squared = 57.007, df = 25, p-value = 0.0002667prop.table(results, margin = 2)##
## 10 13 14 18 19 20
## Brown 0.4000000 0.4000000 0.5333333 0.2333333 0.3666667 0.6285714
## Orange 0.6000000 0.6000000 0.4666667 0.7666667 0.6333333 0.3714286
##
## 21 25 26 30 37 38
## Brown 0.4333333 0.6000000 0.4242424 0.1333333 0.8000000 0.2000000
## Orange 0.5666667 0.4000000 0.5757576 0.8666667 0.2000000 0.8000000
##
## 42 46 5 Deep F1-46 F2-25
## Brown 0.5142857 0.5000000 0.3666667 0.5172414 0.4000000 0.6000000
## Orange 0.4857143 0.5000000 0.6333333 0.4827586 0.6000000 0.4000000
##
## F3-18 F4-34 F5-34 F6-Lilipuna F7-18 F8-10
## Brown 0.2500000 0.4400000 0.2500000 0.5000000 0.2000000 0.4000000
## Orange 0.7500000 0.5600000 0.7500000 0.5000000 0.8000000 0.6000000
##
## F9-5 HIMB
## Brown 0.4000000 0.6000000
## Orange 0.6000000 0.4000000Dominant Symbiont by Depth
merged$Dominant <- ifelse(merged$Dom=="C", 0, 1)
Dom1 <- subset(merged, !is.na(newDepth) & !is.na(Dominant))
results=glm(Dominant~newDepth, family = "binomial", data = merged)
anova(results, test = "Chisq")
logi.hist.plot(Dom1$newDepth, Dom1$Dominant, boxp = FALSE, type = "hist", col="gray", xlabel = "Depth (m)", ylabel = "", ylabel2 = "")
mtext(side = 4, text = "Frequency", line = 3, cex=1)
mtext(side = 4, text = "C D", line = 2, cex = 0.75)
mtext(side = 2, text = "Probability of clade C Symbiont", line = 3, cex = 1)
merged$DepthInt <- cut(merged$Depth..m., breaks = 0:13)
merged$Dominant2 <- ifelse(merged$Dom=="C", 1, 0)
results=table(merged$Dominant2, merged$DepthInt)
results
props <- prop.table(results, margin = 2)
par(mar=c(4, 4, 2, 6), lwd = 0.25)
barplot(props[,1:11], col = c(alpha("red", 0.25), alpha("blue", 0.25)),
xlab = "", ylab = "",
space = 0, xaxs="i", yaxs="i", axisnames = FALSE)
par(lwd=1)
legend("topright", legend=c("C", "D"), fill=c(alpha("blue", 0.25), alpha("red", 0.25)), inset = c(-.2, 0), xpd = NA)
par(new = T)
par(mar=c(4.2, 4, 2, 6))
results=glm(Dominant~Depth..m., family = "binomial", data = merged)
fitted <- predict(results, newdata = list(Depth..m.=seq(0,11,0.1)), type = "response")
plot(fitted~seq(0,11,0.1), xaxs="i", yaxs="i", xlim=c(0,11), ylim=c(0,1), type="l", lwd = 3, xlab="Depth (m)", ylab="Dominant Symbiont Proportion")
Bars indicate relative frequency of clade C vs. D dominance at 1m depth intervals when pooling all samples collected.
Symbiont Community Composition by Depth
merged$Mixture <- ifelse(!merged$Mix=="C" & !merged$Mix=="D", 1, 0)
merged$Mixture2 <- ifelse(!merged$Mix=="C" & !merged$Mix=="D", 0, 1)
results=glm(Mixture~newDepth, family = "binomial", data = merged)
anova(results, test = "Chisq")## Analysis of Deviance Table
##
## Model: binomial, link: logit
##
## Response: Mixture
##
## Terms added sequentially (first to last)
##
##
## Df Deviance Resid. Df Resid. Dev Pr(>Chi)
## NULL 589 817.24
## newDepth 1 75.664 588 741.57 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1results=table(merged$Mixture2, merged$DepthInt)
results##
## (0,1] (1,2] (2,3] (3,4] (4,5] (5,6] (6,7] (7,8] (8,9] (9,10] (10,11]
## 0 172 43 30 34 18 11 3 2 5 2 1
## 1 66 41 60 99 46 20 10 17 12 6 6
##
## (11,12] (12,13]
## 0 1 0
## 1 0 1props <- prop.table(results, margin = 2)
par(mar=c(4, 4, 2, 6), lwd = 0.25)
barplot(props[,1:11], col = c(alpha("red", 0.25), alpha("blue", 0.25)),
xlab = "", ylab = "",
space = 0, xaxs="i", yaxs="i", axisnames = FALSE)
par(lwd=1)
legend("topright", legend=c("Mix", "Non-Mix"), fill=c(alpha("blue", 0.25), alpha("red", 0.25)), inset = c(-.23, 0), xpd = NA)
par(new = T)
par(mar=c(4.2, 4, 2, 6))
results=glm(Mixture~Depth..m., family = "binomial", data = merged)
fitted <- predict(results, newdata = list(Depth..m.=seq(0,11,0.1)), type = "response")
plot(fitted~seq(0,11,0.1), xaxs="i", yaxs="i", xlim=c(0,11), ylim=c(0,1), type="l", lwd = 3, xlab="Depth (m)", ylab="Mixture Proportion")
Color Morph by Depth
merged$Color <- ifelse(merged$Color.Morph=="Orange", 1, 0)
results=glm(Color~newDepth, family = "binomial", data = merged)
anova(results, test = "Chisq")
Color <- subset(merged, !is.na(newDepth) & !is.na(Color))
logi.hist.plot(independ = Color$newDepth, depend = Color$Color, type = "hist", boxp = FALSE, ylabel = "", col="gray", ylabel2 = "", xlabel = "Depth (m)")
mtext(side = 4, text = "Frequency", line = 3, cex=1)
mtext(side = 4, text = "Brown Orange", line = 2, cex = 0.75)
mtext(side = 2, text = "Probability of Orange Color Morph", line = 3, cex = 1)
merged$Color <- ifelse(merged$Color.Morph=="Orange", 0, 1)
results=table(merged$Color, merged$DepthInt)
results
props <- prop.table(results, margin = 2)
par(mar=c(4, 4, 2, 6), lwd = 0.25)
barplot(props[,1:11], col = c(alpha("orange", 0.25), alpha("sienna", 0.25)),
xlab = "", ylab = "",
space = 0, xaxs="i", yaxs="i", axisnames = FALSE)
par(lwd=1)
legend("topright", legend=c("Brown", "Orange"), fill=c(alpha("sienna", 0.25), alpha("orange", 0.25)), inset = c(-.22, 0), xpd = NA)
par(new = T)
par(mar=c(4.2, 4, 2, 6))
merged$Color2 <- ifelse(merged$Color=="0", 1, 0)
results=glm(Color2~Depth..m., family = "binomial", data = merged)
fitted <- predict(results, newdata = list(Depth..m.=seq(0,11,0.1)), type = "response")
plot(fitted~seq(0,11,0.1), xaxs="i", yaxs="i", xlim=c(0,11), ylim=c(0,1), type="l", lwd = 3, xlab="Depth (m)", ylab="Color Morph Proportion")
Bars indicate relative frequency of Brown vs. Orange color morph dominance at 1m depth intervals when pooling all samples collected.
Dominant Symbiont by Color Morph and Reef Area
merged$Reef.Area <- ifelse(merged$Reef.Area!="Top", yes = "Slope", no = "Top")
table(merged$Dom, merged$Color.Morph, merged$Reef.Area)## , , = Slope
##
##
## Brown Orange
## C 226 122
## D 21 79
##
## , , = Top
##
##
## Brown Orange
## C 40 45
## D 12 161model=aov(Dominant~Color.Morph*Reef.Area, data = merged)
Anova(model, type = 2)## Anova Table (Type II tests)
##
## Response: Dominant
## Sum Sq Df F value Pr(>F)
## Color.Morph 21.329 1 134.208 < 2.2e-16 ***
## Reef.Area 14.489 1 91.168 < 2.2e-16 ***
## Color.Morph:Reef.Area 1.780 1 11.201 0.0008612 ***
## Residuals 111.565 702
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Dominant Symbiont by Color Morph and Depth
Because and interactive effect of reef area and color morph was observed and slope is indicative of a depth gradient, the interaction between depth and color morph was tested.
model1=lm(Dominant~Color.Morph*newDepth, data = merged)
Anova(model1, type = 2)## Anova Table (Type II tests)
##
## Response: Dominant
## Sum Sq Df F value Pr(>F)
## Color.Morph 24.863 1 154.391 < 2.2e-16 ***
## newDepth 12.236 1 75.981 < 2.2e-16 ***
## Color.Morph:newDepth 2.592 1 16.097 6.798e-05 ***
## Residuals 94.369 586
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Dominant Symbiont by Depth and Reef Type
model3=aov(Dominant2~Depth..m.*Reef.Type, data = merged)
Anova(model3, type = 2)## Anova Table (Type II tests)
##
## Response: Dominant2
## Sum Sq Df F value Pr(>F)
## Depth..m. 24.214 1 120.3238 < 2.2e-16 ***
## Reef.Type 0.052 1 0.2569 0.612431
## Depth..m.:Reef.Type 1.727 1 8.5815 0.003506 **
## Residuals 141.272 702
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df <- subset(merged, Reef.Type=="Patch")
results=glm(Dominant2~Depth..m., family = "binomial", data = df)
newdata <- list(Depth..m.=seq(0,12,0.01))
par(mar=c(4, 4, 2, 6))
fitted <- predict(results, newdata = newdata, type = "response")
plot(fitted ~ seq(0,12,0.01), ylim = c(0,1), type="l", col="dodgerblue3", lwd=3, xlab="", ylab="", axisnames=FALSE)
abline(h = 0.5, lty=2)
df <- subset(merged, Reef.Type=="Fringe")
results=glm(Dominant2~Depth..m., family = "binomial", data = df)
newdata <- list(Depth..m.=seq(0,12,0.01))
fitted <- predict(results, newdata = newdata, type = "response")
lines(fitted~seq(0,12,0.01), col="brown1", lwd=3)
mtext(side = 1, text = "Depth (m)", line = 3, cex = 1)
mtext(side = 2, text = "Probability of Clade C Symbiont", line = 3, cex = 1)
legend("topright", legend=c("Patch", "Fringe"), fill=c("dodgerblue3", "brown1"), inset = c(-.22, 0), xpd = NA)
Depth at which Orange switches from D to C Dominance
While brown was always C-dominated, orange was observed to switch from D to C dominance. The depth threshold at which this switch occurs was analyzed here.
threshdepth <- function(color) {
df <- subset(merged, Color.Morph==color)
plot(df$Dominant2~df$newDepth, xlab="Depth (m)", ylab = "Probability of Clade C Symbiont",
main=color)
abline(h = 0.5, lty=2)
results=glm(Dominant2~newDepth, family = "binomial", data = df)
pval <- data.frame(coef(summary(results)))$`Pr...z..`[2]
mtext(side=3, text=pval)
newdata <- list(newDepth=seq(0,12,0.01))
fitted <- predict(results, newdata = newdata, type = "response")
lines(fitted ~ seq(0,12,0.01))
thresh <- ifelse(pval < 0.05,
newdata$newDepth[which(diff(sign(fitted - 0.5))!=0)], NA)
return(thresh)
}
sapply(levels(merged$Color.Morph), FUN=threshdepth)
## Brown Orange
## NA 2.7df <- subset(merged, Color.Morph=="Orange")
results=glm(Dominant~newDepth, family = "binomial", data = df)
newdata <- list(newDepth=seq(0,12,0.01))
par(mar=c(4, 4, 2, 6))
fitted <- predict(results, newdata = newdata, type = "response")
plot(fitted ~ seq(0,12,0.01), ylim = c(0,1), type="l", col="orange", lwd=3, xlab="", ylab="", axisnames=FALSE)
abline(h = 0.5, lty=2)
df <- subset(merged, Color.Morph=="Brown")
results=glm(Dominant~newDepth, family = "binomial", data = df)
newdata <- list(newDepth=seq(0,12,0.01))
fitted <- predict(results, newdata = newdata, type = "response")
lines(fitted~seq(0,12,0.01), col="sienna", lwd=3)
mtext(side = 1, text = "Depth (m)", line = 3, cex = 1)
mtext(side = 2, text = "Probability of Clade C Symbiont", line = 3, cex = 1)
legend("topright", legend=c("Brown", "Orange"), fill=c("sienna", "orange"), inset = c(-.22, 0), xpd = NA)
Color Morph by Reef Type and Depth
model2=aov(Color~newDepth*Reef.Type, data = merged)
Anova(model2, type = 2)## Anova Table (Type II tests)
##
## Response: Color
## Sum Sq Df F value Pr(>F)
## newDepth 6.180 1 27.6214 2.069e-07 ***
## Reef.Type 0.132 1 0.5908 0.442436
## newDepth:Reef.Type 1.651 1 7.3813 0.006785 **
## Residuals 131.109 586
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1df <- subset(merged, Reef.Type=="Patch")
results=glm(Color2~newDepth, family = "binomial", data = df)
newdata <- list(newDepth=seq(0,12,0.01))
par(mar=c(4, 4, 2, 6))
fitted <- predict(results, newdata = newdata, type = "response")
plot(fitted ~ seq(0,12,0.01), ylim = c(0,1), type="l", col="dodgerblue3", lwd=3, xlab="", ylab="", axisnames=FALSE)
abline(h = 0.5, lty=2)
df <- subset(merged, Reef.Type=="Fringe")
results=glm(Color2~newDepth, family = "binomial", data = df)
newdata <- list(newDepth=seq(0,12,0.01))
fitted <- predict(results, newdata = newdata, type = "response")
lines(fitted~seq(0,12,0.01), col="brown1", lwd=3)
mtext(side = 1, text = "Depth (m)", line = 3, cex = 1)
mtext(side = 2, text = "Probability of Orange Color Morph", line = 3, cex = 1)
legend("topright", legend=c("Patch", "Fringe"), fill=c("dodgerblue3", "brown1"), inset = c(-.22, 0), xpd = NA)
Figures
Coral Collection Map
KB <- c(21.46087401, -157.809907)
KBMap <- GetMap(center = KB, zoom = 13, maptype = "satellite", SCALE = 2, GRAYSCALE = FALSE)
Latitude=aggregate(Latitude~Reef.ID, data=Symcap, FUN = mean)
Longitude=aggregate(Longitude~Reef.ID, data = Symcap, FUN=mean)
XY<-merge(Latitude, Longitude, by="Reef.ID", all=T)
newcoords <- LatLon2XY.centered(KBMap, XY$Latitude, XY$Longitude, zoom=13)
XY$X <- newcoords$newX
XY$Y <- newcoords$newY
XY <- subset(XY, Reef.ID!="37")
par(oma=c(3,3,0,0))
PlotOnStaticMap(KBMap, XY$Latitude, XY$Longitude, col=153, pch=21, bg="#FF7F50", lwd=2)
axis(1, at = LatLon2XY.centered(KBMap, NA, c(-157.85, -157.81, -157.77))$newX, tcl=0.5, line = 0.5, col = "ghostwhite", col.ticks = "black", lwd = 1, outer = TRUE, labels = c("157.85°W", "157.81°W", "157.77°W"), padj = -2.5, cex.axis = 0.75)
axis(2, at = LatLon2XY.centered(KBMap, c(21.42, 21.46, 21.50), NA)$newY, tcl=0.5, line = 0.5, col = "ghostwhite", col.ticks = "black", lwd = 1, outer = TRUE, labels = c("21.42°N", "21.46°N", "21.50°N"), padj = 0.5, hadj = 0.60, las = 1, cex.axis = 0.75)
par(new=T, mar=c(11.8,19,0,0))
HI <- readOGR("coast_n83.shp", "coast_n83")
HI <- spTransform(HI, CRS("+proj=longlat +datum=NAD83"))
plot(HI, xlim=c(-158.3, -157.6), ylim=c(21.35, 21.6), lwd=0.4, col="gray", bg="white")
rect(-157.9, 21.41, -157.75, 21.53)
box()
Depth Influence on Dominant Symbiont and Color Morph
par(mfrow=c(3,1))
merged$DepthInt <- cut(merged$Depth..m., breaks = 0:13)
merged$Dominant <- ifelse(merged$Dom=="C", 0, 1)
merged$Dominant2 <- ifelse(merged$Dom=="C", 1, 0)
results=table(merged$Dominant2, merged$DepthInt)
results
props <- prop.table(results, margin = 2)
par(mar=c(2, 4, 2, 6), lwd = 0.25)
barplot(props[,1:11], col = c(alpha("red", 0.25), alpha("blue", 0.25)),
xlab = "", ylab = "",
space = 0, xaxs="i", yaxs="i", axisnames = FALSE)
par(lwd=1)
legend("topright", legend=c("C", "D"), fill=c(alpha("blue", 0.25), alpha("red", 0.25)), inset = c(0, 0), xpd = NA)
par(new = T)
par(mar=c(2.1, 4, 2, 6))
results=glm(Dominant~Depth..m., family = "binomial", data = merged)
fitted <- predict(results, newdata = list(Depth..m.=seq(0,11,0.1)), type = "response")
plot(fitted~seq(0,11,0.1), xaxs="i", yaxs="i", xlim=c(0,11), ylim=c(0,1), type="l", lwd = 3, xlab="", ylab="Probability of D-Dominance")
merged$Color <- ifelse(merged$Color.Morph=="Orange", 0, 1)
results=table(merged$Color, merged$DepthInt)
results
props <- prop.table(results, margin = 2)
par(mar=c(3, 4, 1, 6), lwd = 0.25)
barplot(props[,1:11], col = c(alpha("orange", 0.25), alpha("sienna", 0.25)),
xlab = "", ylab = "Probability of Orange-Dominance",
space = 0, xaxs="i", yaxs="i", axisnames = FALSE)
par(lwd=1)
legend("topright", legend=c("Brown", "Orange"), fill=c(alpha("sienna", 0.25), alpha("orange", 0.25)), inset = c(0, 0), xpd = NA)
par(new = T)
par(mar=c(3.1, 4, 1, 6))
merged$Color2 <- ifelse(merged$Color=="0", 1, 0)
results=glm(Color2~Depth..m., family = "binomial", data = merged)
fitted <- predict(results, newdata = list(Depth..m.=seq(0,11,0.1)), type = "response")
plot(fitted~seq(0,11,0.1), xaxs="i", yaxs="i", xlim=c(0,11), ylim=c(0,1), type="l", lwd = 3, xlab="", ylab="")
df <- subset(merged, Color.Morph=="Orange")
results=glm(Dominant~newDepth, family = "binomial", data = df)
newdata <- list(newDepth=seq(0,11,0.01))
par(mar=c(4, 4, 0, 6))
fitted <- predict(results, newdata = newdata, type = "response")
plot(fitted ~ seq(0,11,0.01), ylim = c(0,1), type="l", col="orange", lwd=3, xlab="Depth (m)", ylab="Probabilty of D-Dominance", axisnames=FALSE, xaxs = "i", yaxs = "i")
abline(h = 0.5, lty=2)
df <- subset(merged, Color.Morph=="Brown")
results=glm(Dominant~newDepth, family = "binomial", data = df)
newdata <- list(newDepth=seq(0,11,0.01))
fitted <- predict(results, newdata = newdata, type = "response")
lines(fitted~seq(0, 11, 0.01), col="sienna", lwd=3)
#legend("topright", legend=c("Brown", "Orange"), fill=c("sienna", "orange"), inset = c(-.13, 0), xpd = NA)
results=table(merged$Dominant2, merged$Color.Morph)
chisq.test(results)
prop.table(results, margin = 2)
par(new=T, mar=c(10, 10, .5, 6.3))
barplot(prop.table(results, margin = 2), col = c(alpha("red", 0.35), alpha("blue", 0.35)), xlab = "", ylab = "", yaxt = 'n')
#legend("topright", legend=c("C", "D"), fill=c(alpha("blue", 0.35), alpha("red", 0.35)), inset = c(0, 0), xpd = NA)